
%matplotlib inline

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import cm

import ipyparallel as ipp

from time import time
from datetime import datetime

import motif as mf

from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn.decomposition import PCA
from sklearn.utils import shuffle
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.model_selection import train_test_split, cross_val_score, cross_validate, RepeatedKFold

from scipy.stats import spearmanr
from scipy.stats import pearsonr

Intel(R) Extension for Scikit-learn* enabled (https://github.com/intel/scikit-learn-intelex)


### set parameters for the motif analysis

PROTEIN_NAME='T7 primase'
PROT_CONC=5 # free protein concentration at binding reation; PBM typically 0.1 and RNACompete typically 0.002
BOTH_STRANDS=False # wheter both strands are present for binding; True if double-stranded DNA or RNA is used as probes

STAGES=mf.stage(protein=PROTEIN_NAME)


### read data

dfprobes_raw=pd.read_csv('./data/T7/chip_B_favor.csv', sep=';')
print('Columns of imported Data File: %s'%dfprobes_raw.columns)

Columns of imported Data File: Index(['0_nuc', '1_nuc', '2_nuc', '3_nuc', '4_nuc', '5_nuc', '6_nuc', '7_nuc',
       '8_nuc', '9_nuc', '10_nuc', '11_nuc', '12_nuc', '13_nuc', '14_nuc',
       '15_nuc', '16_nuc', '17_nuc', '18_nuc', '19_nuc', '20_nuc', '21_nuc',
       '22_nuc', '23_nuc', '24_nuc', '25_nuc', '26_nuc', '27_nuc', '28_nuc',
       '29_nuc', '30_nuc', '31_nuc', '32_nuc', '33_nuc', '34_nuc', '35_nuc',
       'prim_only_score', 'prim', 'poly', 'seq'],
      dtype='object')


### select columns for probe sequence and signal

column_sequence='seq'
column_signal='prim_only_score'
#background_signal='mean_background_intensity'
background_signal=None                             #set to None if not needed

#basic preprocessing
dfprobes_raw[column_signal]=dfprobes_raw[column_signal].apply(lambda a: np.NaN if a==' ' else a)
dfprobes_raw[column_sequence]=dfprobes_raw[column_sequence].apply(lambda a: np.NaN if a=='nan' else a)
dfprobes_raw=dfprobes_raw.dropna()

#construct new dataframe with only necessary data
if type(background_signal)==type(None):
    dfprobes=pd.DataFrame({'seq':dfprobes_raw[column_sequence].astype(str),
                           'signal binding': dfprobes_raw[column_signal].astype(np.float32)}) #rebuild dataframe
else:
    dfprobes=pd.DataFrame({'seq':dfprobes_raw[column_sequence].astype(str),
                           'signal': dfprobes_raw[column_signal].astype(np.float32), 
                           'background':dfprobes_raw[background_signal].astype(np.float32)}) #rebuild dataframe
    dfprobes['signal binding']=dfprobes['signal']-dfprobes['background']
# display main properties of data set

dfprobes['signal binding']=dfprobes['signal binding']**2  # as raw data was squared

dfprobes['signal binding'].plot(figsize=(15,5))
dfprobes.describe()

### check type of nucleic acid

dfprobes['seq']=dfprobes['seq'].apply(lambda seq: seq.upper().replace(" ", ""))  #upper and remove blanks
dfprobes['RNA']=dfprobes['seq'].apply(lambda seq: all(char in 'ACGU' for char in seq))
dfprobes['DNA']=dfprobes['seq'].apply(lambda seq: all(char in 'ACGT' for char in seq))
non_RNA_counts=len(dfprobes[dfprobes['RNA']==False])
non_DNA_counts=len(dfprobes[dfprobes['DNA']==False])

if non_RNA_counts<non_DNA_counts:
    NUC_TYPE='RNA'; print('I: RNA probes detected!')
else: NUC_TYPE='DNA'; print('I: DNA probes detected!')
    
if NUC_TYPE=='RNA' and non_RNA_counts!=0:
    print('E: The probe sequences appear to be RNA, however there are some non-RNA nucleotides in the sequences.')
    print('E: Please check the following sequnces %s'%dfprobes[dfprobes['RNA']==False])

if NUC_TYPE=='DNA' and non_DNA_counts!=0:
    print('E: The probe sequences appear to be RNA, however there are some non-RNA nucleotides in the sequences.')
    print('E: Please check the following sequnces %s'%dfprobes[dfprobes['DNA']==False])

I: DNA probes detected!


### option to add a constant sequence at the 3' end and 5' end
sequence_to_be_added_5='' 
sequence_to_be_added_3='GTCTTG'        # standard PBM arrays: CCTGTGTGAAATTGTTATCCGCTCT T7 array: GTCTTGA..
dfprobes['seq']=sequence_to_be_added_5.upper()+dfprobes['seq']+sequence_to_be_added_3.upper()
print(f"I: The nucleotide sequences {sequence_to_be_added_5.upper()} has been added to the 5' end all probe sequences.")
print(f"I: The nucleotide sequences {sequence_to_be_added_3.upper()} has been added to the 3' end all probe sequences.")

I: The nucleotide sequences  has been added to the 5' end all probe sequences.
I: The nucleotide sequences GTCTTG has been added to the 3' end all probe sequences.


### egalize length
dfprobes['seq_length']=dfprobes['seq'].apply(len)    

if max(dfprobes['seq_length'])!=min(dfprobes['seq_length']):
    print('I: Probes length is not uniform, detected range: %i ..%i' %(min(dfprobes['seq_length']),max(dfprobes['seq_length'])))
    max_length=max(dfprobes['seq_length'])
    dfprobes['padded_sequence']=dfprobes['seq'].apply(lambda seq: seq+((max_length-len(seq))*'-'))
    print("I: Probe sequences have been padded at the 5' to the uniform length of %i nucleotides" %max_length)
else:
    print('I: Probe sequences have the uniform length of %i nucleotides' %(dfprobes['seq_length'].median()))
    dfprobes['padded_sequence']=dfprobes['seq']

print('I: Total datasets contains %i sequences.' % len(dfprobes))

# visualize composition of each position
df_nucleotides=mf.split_sequence_in_nucleotides(dfprobes['padded_sequence'])
dfcount=pd.DataFrame(index=['A', 'C', 'G', 'T', 'U', '-'])
for column in df_nucleotides:
    dfcount[column]=df_nucleotides[column].value_counts() 
dfcount=dfcount.fillna(0) #zeros for NaN
dfcount.transpose().plot(figsize=(15,5), kind='bar')
print('I: Visualisation of the base composition per position')
print('I: If positions are invariant they can be removed before sequence analysis.')

I: Probe sequences have the uniform length of 42 nucleotides
I: Total datasets contains 3149 sequences.
I: Visualisation of the base composition per position
I: If positions are invariant they can be removed before sequence analysis.


# You may remove invariant continuos positions by adjusting the slicing. 
# It is recommended to leave a few invariant positions to allow for binding events
# between the variable and constant part of the probes.

dfprobes['padded_sequence']=dfprobes['padded_sequence'].apply(lambda s:s[:43]) ### <==== do the slicing here

# visualize composition of each position
print('I: Visualisation of the base composition per position after slicing.')
df_nucleotides=mf.split_sequence_in_nucleotides(dfprobes['padded_sequence'])
dfcount=pd.DataFrame(index=['A', 'C', 'G', 'T', 'U', '-'])
for column in df_nucleotides:
    dfcount[column]=df_nucleotides[column].value_counts() 
dfcount=dfcount.fillna(0) #zeros for NaN
dfcount.transpose().plot(figsize=(15,5), kind='bar')
plt.show()

### Preparation for later classification
mean=dfprobes['signal binding'].mean()
std=dfprobes['signal binding'].std()
THRESHOLD=mean+2*std   
dfprobes['positive probe']=dfprobes['signal binding'].apply(lambda s: True if s>THRESHOLD else False)

print('I: The whole dataset has been used to set the threshold for a positive probe.')
print('T: The threshold is %f' %THRESHOLD)
print(f"I: {len(dfprobes[dfprobes['positive probe']])} probes of {len(dfprobes)} are above threshold.")

I: Visualisation of the base composition per position after slicing.

I: The whole dataset has been used to set the threshold for a positive probe.
T: The threshold is 0.663948
I: 196 probes of 3149 are above threshold.


### Shuffle and prepare dataset for training and testing

# shuffle 
dfprobes = shuffle(dfprobes)

# display histogramms of dataset
dfprobes['signal binding'].plot(kind='hist', bins=25).axvline(x=THRESHOLD, color='r', linestyle='-.', lw=0.5, label='threshold classification')

# complete data
X=mf.hotencode_sequence(dfprobes['padded_sequence'], nuc_type=NUC_TYPE)
y=np.array(dfprobes['signal binding'])


#### Perfrom GridCV Search for exploration of the motif length and other model parameters 
# primary goal: identify the minimum motif length which gives a good r-value
# optional: allow also for global optimization to verify whether the local optimization is good enough
# optional: vary G0 to find a good regression model
# optional: fitG0=True. This option adjust G0, so that maximal binding is around 1 (prevents saturation of probes by multiple binding events)


model_grid=mf.findmotif(protein_conc=PROT_CONC, both_strands=BOTH_STRANDS, fit_G0=True)

# prepare grid search over parameters
param_grid = {"motif_length": [1,2,3,4,5,6]}     # choose sensible range for length of motif and other parameters

# run grid search
grid_search = GridSearchCV(model_grid, param_grid=param_grid, verbose=2, 
                           cv=RepeatedKFold(n_splits=5, n_repeats=10),
                           n_jobs=-1,
                           return_train_score=True)

start = time()
grid_search.fit(X, y)
df_grid=pd.DataFrame(grid_search.cv_results_)

print("I: GridSearchCV took %.2f hours for %d candidate parameter settings."
    % ((time() - start)/3600, len(grid_search.cv_results_["params"])))
print('I: number of samples: %i' %len(X))

df_grid=pd.DataFrame(grid_search.cv_results_)

print('I: Plot of r vs motif length')
df_grid['r(train)']=df_grid['mean_train_score'].apply(np.sqrt)
df_grid['r(test)']=df_grid['mean_test_score'].apply(np.sqrt)
df_grid['std(train)']=df_grid['std_train_score'].apply(np.sqrt)
df_grid['std(test)']=df_grid['std_test_score'].apply(np.sqrt)


fig, ax=plt.subplots()
df_grid.plot(ax=ax,kind='scatter', x='param_motif_length', y='r(train)', yerr='std(train)', figsize=(5,3), c='blue').set_xticks(param_grid["motif_length"])
df_grid.plot(ax=ax,kind='scatter', x='param_motif_length', y='r(test)', yerr='std(test)', c='red')
ax.set_ylabel('r')
ax.legend(['train', 'test'])
plt.show()
df_grid[['param_motif_length','r(test)', 'r(train)']]

Fitting 50 folds for each of 6 candidates, totalling 300 fits
I: GridSearchCV took 14.19 hours for 6 candidate parameter settings.
I: number of samples: 3149
I: Plot of r vs motif length


### run a number of identical optimizations with motif length found during grid search
### goal: find best motif through repetition, judge stabiltiy of optimization

CORE_MOTIF_LENGTH=4  # adjust core motif length if needed, motif length can be changed later

# prepare for ipyparallel
number_of_optimizations = 200
model_list = [mf.findmotif(motif_length=CORE_MOTIF_LENGTH, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS, fit_G0=True)] * number_of_optimizations
X_list = [X] * number_of_optimizations
y_list = [y] * number_of_optimizations


def single_job(model, X, y):
    model.fit(X, y)
    return {'model':model}

# run the optimizations on ipp.cluster
start = time()
with ipp.Cluster(log_level=40) as rc:
    rc[:].use_pickle()
    view = rc.load_balanced_view()
    asyncresult = view.map_async(single_job, model_list, X_list, y_list)
    asyncresult.wait_interactive()
    result = asyncresult.get()
print("I: Optimization took %.2f hours." % ((time() - start) / 3600))


  
# assemble results and analyze
df_repetitions=pd.DataFrame(result)
df_repetitions['r']=df_repetitions['model'].apply(lambda e: mf.linregress(e.predict(X),y).rvalue)
df_repetitions['G0']=df_repetitions['model'].apply(lambda e: e.finalG0_)
df_repetitions['max binding']=df_repetitions['model'].apply(lambda e: e.max_binding_fit)
df_repetitions['min binding']=df_repetitions['model'].apply(lambda e: e.min_binding_fit)
df_repetitions['ratio'] = df_repetitions['model'].apply(lambda e: e.ratio)
df_repetitions['energies']=df_repetitions['model'].apply(lambda e: e.energies_)
df_repetitions['information']=df_repetitions['model'].apply(lambda e: mf.energies2information(e.energies_))


# display results of the ensemble of optimizations
print('I: Results of the repeated motif finding, sorted according to the regression coefficient')
df_repetitions.sort_values('r', ascending=False, inplace=True)
mf.display_df(df_repetitions, nuc_type=NUC_TYPE)

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I: Optimization took 10.22 hours.
I: Results of the repeated motif finding, sorted according to the regression coefficient


df_repetitions=df_repetitions[:-int(len(df_repetitions)/20)] #delete 5% worst


### compare energy matrices of ensemble using PCA
print('I: Histogram of the regression coefficients r obtained by repeated optimizaion with the subset.')
df_repetitions['r'].plot(kind='hist')
plt.show()

pca = PCA(n_components=2)
pca_2c=pca.fit_transform(df_repetitions['energies'].tolist())    
df_repetitions[['PCA1', 'PCA2']]=pca_2c
print('I: 2-dimensional PCA explained %i %% of variance.' %(sum(pca.explained_variance_ratio_)*100))
if sum(pca.explained_variance_ratio_)<0.5:
      print('W: 2-dimensional PCA explained only %i %% of variance' %(sum(pca.explained_variance_ratio_)*100))

print('I: Visualization of the PCA with the regression quality by color.')        
df_repetitions.plot(x='PCA1', y='PCA2', kind='scatter', c='r',cmap=cm.coolwarm, edgecolors='black', linewidths=0.3)

I: Histogram of the regression coefficients r obtained by repeated optimizaion with the subset.

I: 2-dimensional PCA explained 80 % of variance.
I: Visualization of the PCA with the regression quality by color.

/home/GLipps/.local/lib/python3.8/site-packages/sklearn/utils/deprecation.py:101: FutureWarning: Attribute `n_features_` was deprecated in version 1.2 and will be removed in 1.4. Use `n_features_in_` instead.
  warnings.warn(msg, category=FutureWarning)

<matplotlib.axes._subplots.AxesSubplot at 0x7fb275d513d0>


mf.display_df(df_repetitions, nuc_type=NUC_TYPE)


# visualisation of the motif with the highest r
print('I: Best motif according to r from the repeated optimizations.')
print('I: PCA components: %i, %i' %(df_repetitions.iloc[0]['PCA1'], df_repetitions.iloc[0]['PCA2']))
model_best_repetition=df_repetitions.iloc[0]['model']
model_best_repetition.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE) 
# store results and display stages
STAGES.append('best repetition', model_best_repetition)
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

I: Best motif according to r from the repeated optimizations.
I: PCA components: 17988, -39
I: energy matrix and logos:

       A      C     G     T
0 -6305  12275 -7599  1630
1  8790  -5524  4206 -7472
2   317  -2245  2231  -303
3  -829    993   978 -1143

I: summed absolute energies of each position:
0    27811
1    25994
2     5097
3     3945
dtype: int64

I: averaged summed energy over all positions: 15712
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -8569 +/- 9131
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.30577 .. 0.96111 (ratio: 3.1)
I: number of probes: 3149
I: Pearson Correlation  r: 0.7091
I: mean absolute error: 0.1027

I: Classification performance AUROC: 0.8296


# refit model to minimize mae
last_model=STAGES.df.at[max(STAGES.df.index),'model']
refitted_model_best_repetition=model_best_repetition.refit_mae(X,y)

# print & display main results
refitted_model_best_repetition.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)

# store results and display stages
STAGES.append('mae optimzed', refitted_model_best_repetition)
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

Optimization terminated successfully.
         Current function value: 0.098388
         Iterations: 8
         Function evaluations: 1634
I: energy matrix and logos:

       A      C     G     T
0 -5884  12970 -7518   431
1  8824  -5437  4115 -7502
2   105  -2208  2341  -238
3  -482    976   745 -1238

I: summed absolute energies of each position:
0    26804
1    25880
2     4894
3     3442
dtype: int64

I: averaged summed energy over all positions: 15255
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -8575 +/- 9400
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.31222 .. 0.95738 (ratio: 3.1)
I: number of probes: 3149
I: Pearson Correlation  r: 0.6958
I: mean absolute error: 0.0984

I: Classification performance AUROC: 0.8205


### Based on the motif of CORE_MOTIF_LENGTH analyze the neigbouring positions 
### whether their inclusion can improve the quality of the motif
df_positions=model_best_repetition.investigate_extension_parallel(X, y, end5=3, end3=3, nuc_type=NUC_TYPE)

list_positions=df_positions.index[df_positions['+2%']].tolist()+[0] # list of positions with an increase of2% and default position 0
ext5=-min(list_positions)
ext3=max(list_positions)
print("I: It is suggested to extend the core motif at the 5' end by %i and at the 3' end by %i positions." %(ext5, ext3))

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I: Optimization took 0.02 hours.

I: It is suggested to extend the core motif at the 5' end by 0 and at the 3' end by 0 positions.


### fit & predict optimization starting with extended energy matrix if extension appears to improve prediction

if ext5+ext3!=0: #extension suggestion from previous analysis of the bordering positions
    expanded_energies=model_best_repetition.energies_
    # append energies of single-optimized bordering positions to energies of central part
    if ext5!=0:
        energies_5=np.concatenate(df_positions['energies'][(df_positions.index<0) & (df_positions.index>=-ext5)].to_numpy())
        expanded_energies=np.concatenate((energies_5, expanded_energies))
    if ext3!=0:
        energies_3=np.concatenate(df_positions['energies'][(df_positions.index<=ext3) & (df_positions.index>0)].to_numpy().flatten())
        expanded_energies=np.concatenate((expanded_energies,  energies_3))

    mf.energies2logo(expanded_energies, nuc_type=NUC_TYPE)
    print('I: Optimization started with following extended motif.')
    expanded_motif_length=len(expanded_energies)//4
    
    
    model_extended=mf.findmotif(motif_length=expanded_motif_length, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS,
                   start=expanded_energies, fit_G0=True)

    start = time()
    model_extended.fit(X,y)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    model_extended.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)

    # store results and display stages
    STAGES.append('extended', model_extended)
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)
else:
    print('I: Motif is not extended based on previous analysis.')

I: Motif is not extended based on previous analysis.


### fit & predict optimization starting with extended energy matrix plus one bordering position on each side if current bordering position exceed the information of 0.25

last_model=STAGES.df.at[max(STAGES.df.index),'model']
I_5=mf.energies2information(last_model.energies_[0:4])>=0.25 #sufficient information content of 5' end position
I_3=mf.energies2information(last_model.energies_[-4:])>=0.25 #sufficient information content of 3' end position

if I_5 or I_3:
    print('I: At least one of the bordering positions of the current motif has an information content of at least 0.25. Extending.')
    expanded_energies_with_border=mf.modify_energies(last_model.energies_, end5=I_5, end3=I_3)  
    mf.energies2logo(expanded_energies_with_border, nuc_type=NUC_TYPE)
    motif_length_with_border=len(expanded_energies_with_border)//4

    model_with_border=mf.findmotif(motif_length=motif_length_with_border, protein_conc=PROT_CONC, both_strands=BOTH_STRANDS,
                   start=expanded_energies_with_border, fit_G0=True)


    start = time()
    model_with_border.fit(X,y)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    model_with_border.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)

    # store results and display stages
    STAGES.append('expanded, border', model_with_border)
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)
else:
    print('I: Both bordering positions of the found motif have an information content below 0.25. No futher optimization required.')

I: At least one of the bordering positions of the current motif has an information content of at least 0.25. Extending.

Optimization took 0.25 hours.
I: energy matrix and logos:

       A      C     G     T
0   467   -139  -407    79
1 -6500  12755 -7629  1374
2  8439  -5499  4236 -7176
3   261  -2117  2001  -145
4  -940    976  1008 -1044

I: summed absolute energies of each position:
0     1094
1    28260
2    25351
3     4526
4     3970
dtype: int64

I: averaged summed energy over all positions: 12640
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -8898 +/- 9231
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.25453 .. 0.98793 (ratio: 3.9)
I: number of probes: 3149
I: Pearson Correlation  r: 0.7171
I: mean absolute error: 0.1013

I: Classification performance AUROC: 0.8313


last_model=STAGES.df.at[max(STAGES.df.index),'model']
df_relevant_positions=last_model.explore_positions(X, y)
list_positions=df_relevant_positions.index[df_relevant_positions['-2%']].tolist() # list of positions with an increase of2% and default position 0
start_relevant=min(list_positions)
end_relevant=max(list_positions)
red5=-start_relevant
red3=end_relevant-len(df_relevant_positions)+1
print('I: The analysis suggests, that positions between %i to %i contribute significantly to the motif' %(start_relevant, end_relevant))
last_model=STAGES.df.at[max(STAGES.df.index),'model']

if (end_relevant-start_relevant+1)in STAGES.df['motif length'].to_list():
    print('I: No need for a further optimization. An optimization with motif length of %i has already been done.' %(end_relevant-start_relevant+1))
    print('I: Checking whether G0 has been chosen correctly.')
    last_model.investigate_G0(X, y)
else:
    print('I: Bordering positions only marginally contributing towards regression quality are dropped.')
    print('I: New start energy for motif optimization:')
    start_final_model=mf.modify_energies(last_model.energies_, end5=red5, end3=red3)
    mf.energies2logo(start_final_model, nuc_type=NUC_TYPE)
    final_model=mf.findmotif(motif_length=len(start_final_model)//4, protein_conc=PROT_CONC, 
                             both_strands=BOTH_STRANDS, start=start_final_model)

    start = time()
    final_model.fit(X,y)
    print("Optimization took %.2f hours." % ((time() - start)/3600))

    # print & display main results
    final_model.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)
    
    print('I: Checking whether G0 has been chosen correctly.')
    final_model.investigate_G0(X, y)

    # store results and display stages
    STAGES.append('shrinked', final_model)
    mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

I: The analysis suggests, that positions between 1 to 4 contribute significantly to the motif
I: No need for a further optimization. An optimization with motif length of 4 has already been done.
I: Checking whether G0 has been chosen correctly.
I: Current G0 = -8730 J/mol (see red broken line in figure below) with r = 0.717.
I: Maximal r is 0.718 at G0=-5730 J/mol (see green broken line below).
I: Maximal occupancy of 2 is reached at G0=-10730 J/mol (see blue broken line below).
I: Maximal occupancy of 0.2 is reached at G0=-4730 J/mol (see blue broken line below).

I: G0 is in a range leading to maximal probe occupancy between 0.2 and 2. Good.
I: Maximal r is close to r achieved with current G0. Good.


# refit model to minimize mae
last_model=STAGES.df.at[max(STAGES.df.index),'model']
refitted_model=last_model.refit_mae(X,y)

# print & display main results
refitted_model.analyse_motif(X,y, THRESHOLD, nuc_type=NUC_TYPE)

# store results and display stages
STAGES.append('final mae optimzed', refitted_model)
mf.display_df(STAGES.df, nuc_type=NUC_TYPE)

Optimization terminated successfully.
         Current function value: 0.096996
         Iterations: 14
         Function evaluations: 3483
I: energy matrix and logos:

       A      C     G     T
0   354    -41  -284   -28
1 -6129  13736 -7781   175
2  8557  -5309  4058 -7305
3  -484  -2105  2435   154
4  -479   1055   573 -1149

I: summed absolute energies of each position:
0      709
1    27823
2    25231
3     5178
4     3258
dtype: int64

I: averaged summed energy over all positions: 12440
I: Mean and Standard Deviation for the Free Energy G to all subsequences of all probes: -8888 +/- 9642
I: Plot of the Occupancy of a subsite as the function of the Free Energy G 
   overlaid with the distribution of the Free Energy of all subsites.
I: There shall be only a small overlap of both curves. i.e. only the most negative Free Energies
    lead to a measurable occupancy.

I: Calculated occupancy over all subsite of a single probe:
   binding:  0.31321 .. 1.06356 (ratio: 3.9)
I: number of probes: 3149
I: Pearson Correlation  r: 0.6981
I: mean absolute error: 0.0970

I: Classification performance AUROC: 0.8165


STAGES.df['energies'].apply(mf.energies2information)

0    2.372494
1    2.338378
2    2.306898
3    2.309537
Name: energies, dtype: float64


STAGES.df.to_json('%s_%s-%s-%s_%s-%s.json' %(PROTEIN_NAME, datetime.now().year, datetime.now().month,datetime.now().day , datetime.now().hour, datetime.now().minute))

	param_motif_length	r(test)	r(train)
0	1	0.173419	0.187203
1	2	0.658905	0.660726
2	3	0.666561	0.670032
3	4	0.682361	0.689046
4	5	0.691722	0.697365
5	6	0.694235	0.701794

	model	r	G0	max binding	min binding	ratio	energies	information
184	suppressed	0.709102	-8369.682846	0.961111	3.057673e-01	3.143276e+00	-6305,..	2.372494
60	suppressed	0.709079	-6483.433347	0.969193	3.052673e-01	3.174901e+00	-6098,..	2.434594
68	suppressed	0.708971	-9111.459686	0.970340	3.117814e-01	3.112246e+00	-5960,..	2.360772
63	suppressed	0.708837	-8917.596818	0.968301	3.115061e-01	3.108449e+00	-4911,..	2.374628
144	suppressed	0.708727	-9858.288782	0.975751	3.108550e-01	3.138928e+00	-6247,..	2.281839
114	suppressed	0.708587	-10537.550819	0.977608	3.110741e-01	3.142686e+00	-5771,..	2.270755
108	suppressed	0.708546	-11716.668946	0.982724	3.243494e-01	3.029831e+00	-5044,..	2.231729
107	suppressed	0.708505	-10799.940918	0.983687	3.207868e-01	3.066482e+00	-4894,..	2.240343
98	suppressed	0.708344	-9607.492522	0.961975	3.210375e-01	2.996457e+00	-6141,..	2.295675
43	suppressed	0.708297	-12027.574721	0.980347	3.232646e-01	3.032646e+00	-4163,..	2.262535
69	suppressed	0.708239	-12542.382842	0.976880	3.270506e-01	2.986939e+00	-4521,..	2.152745
75	suppressed	0.708180	-10861.826932	0.971977	3.216252e-01	3.022080e+00	-6545,..	2.108372
171	suppressed	0.708176	-12574.545868	0.961221	3.168588e-01	3.033594e+00	-4297,..	2.181432
130	suppressed	0.708125	-10091.505729	0.960229	3.288380e-01	2.920067e+00	-6843,..	2.222277
96	suppressed	0.708117	-12468.372975	0.978621	3.347994e-01	2.923006e+00	-4713,..	2.165810
32	suppressed	0.708044	-10567.029564	0.990502	3.309813e-01	2.992622e+00	-6730,..	2.128858
94	suppressed	0.708014	-6377.124055	0.975092	3.002550e-01	3.247548e+00	-7061,..	2.493042
14	suppressed	0.707989	-12696.448826	0.978061	3.171902e-01	3.083517e+00	-3945,..	2.186752
44	suppressed	0.707945	-10909.425345	0.978234	3.277142e-01	2.985023e+00	-6109,..	2.180312
49	suppressed	0.707899	-11768.069798	0.976074	3.122815e-01	3.125624e+00	-5124,..	2.075452
28	suppressed	0.707751	-10773.722431	0.989636	3.263602e-01	3.032342e+00	-6965,..	2.027353
142	suppressed	0.707746	-11610.950337	0.966895	3.193371e-01	3.027819e+00	-5455,..	2.148604
21	suppressed	0.707740	-12036.418943	0.984355	3.345586e-01	2.942251e+00	-5711,..	2.061603
8	suppressed	0.707738	-13148.663195	0.978205	3.146517e-01	3.108849e+00	-3911,..	2.085305
195	suppressed	0.707713	-9692.825219	0.978272	3.199624e-01	3.057459e+00	-7421,..	2.110664
149	suppressed	0.707620	-13161.544905	0.973257	3.387901e-01	2.872742e+00	-4232,..	2.094864
87	suppressed	0.707618	-13553.509385	0.977256	3.327196e-01	2.937175e+00	-3862,..	2.075937
104	suppressed	0.707603	-12612.377303	0.975036	3.181669e-01	3.064541e+00	-4193,..	2.104714
152	suppressed	0.707585	-11501.727201	0.973607	3.443238e-01	2.827590e+00	-5912,..	2.155174
141	suppressed	0.707575	-9973.717176	0.985053	3.404881e-01	2.893062e+00	-7111,..	2.125851
16	suppressed	0.707554	-13159.914853	0.978340	3.379948e-01	2.894541e+00	-4103,..	2.122826
146	suppressed	0.707519	-11236.377197	0.979721	3.290213e-01	2.977684e+00	-6075,..	2.101092
65	suppressed	0.707508	-13867.033125	0.978684	3.369704e-01	2.904360e+00	-4009,..	1.985818
151	suppressed	0.707465	-12529.011975	0.983077	3.272703e-01	3.003867e+00	-3756,..	2.150554
13	suppressed	0.707361	-12294.144056	0.969659	3.328952e-01	2.912805e+00	-5368,..	2.036012
120	suppressed	0.707344	-14248.122927	0.974966	3.397490e-01	2.869665e+00	-3806,..	1.933827
183	suppressed	0.707320	-13547.375868	0.991946	3.190458e-01	3.109102e+00	-4466,..	1.959040
192	suppressed	0.707311	-14071.357127	0.987226	3.426005e-01	2.881567e+00	-4029,..	1.942493
172	suppressed	0.707291	-14202.033901	0.985084	3.274552e-01	3.008301e+00	-3611,..	1.929793
109	suppressed	0.707267	-13278.828687	0.990597	3.262230e-01	3.036563e+00	-4060,..	2.121135
29	suppressed	0.707244	-11582.318621	0.974590	3.222792e-01	3.024056e+00	-5820,..	2.091526
70	suppressed	0.707232	-14426.213016	0.977949	3.314377e-01	2.950627e+00	-3647,..	1.908878
24	suppressed	0.707171	-13452.424348	0.969906	3.256196e-01	2.978647e+00	-4469,..	1.867069
31	suppressed	0.707094	-14150.017156	0.972750	3.441585e-01	2.826460e+00	-3919,..	1.960341
188	suppressed	0.707090	-13118.532865	0.984173	3.203848e-01	3.071847e+00	-3875,..	2.115472
62	suppressed	0.707049	-14663.897962	0.976094	3.358426e-01	2.906403e+00	-3559,..	1.857300
34	suppressed	0.707022	-14141.075086	0.973570	3.415120e-01	2.850763e+00	-4104,..	1.887372
161	suppressed	0.706965	-14284.886682	0.974979	3.323667e-01	2.933444e+00	-3876,..	1.898711
78	suppressed	0.706961	-13842.987845	0.981583	3.305896e-01	2.969190e+00	-4185,..	1.875746
41	suppressed	0.706938	-14000.601372	0.985296	3.476028e-01	2.834546e+00	-4135,..	1.951028
89	suppressed	0.706883	-14046.231784	0.970092	3.480889e-01	2.786907e+00	-3878,..	1.977826
101	suppressed	0.706842	-14628.003671	0.974030	3.352377e-01	2.905489e+00	-3577,..	1.819633
30	suppressed	0.706813	-14424.234893	0.970130	3.446618e-01	2.814729e+00	-3763,..	1.887071
119	suppressed	0.706809	-14484.700741	0.978064	3.240691e-01	3.018073e+00	-3443,..	1.896427
113	suppressed	0.706800	-10651.474175	1.001419	3.185569e-01	3.143612e+00	-6723,..	1.865427
80	suppressed	0.706772	-14603.283640	0.977040	3.356094e-01	2.911243e+00	-3647,..	1.819496
166	suppressed	0.706756	-13080.277537	0.967184	3.261860e-01	2.965130e+00	-3613,..	1.940005
158	suppressed	0.706714	-13754.512853	0.973007	3.125949e-01	3.112678e+00	-4105,..	1.823113
150	suppressed	0.706676	-13905.816204	0.980039	3.233605e-01	3.030794e+00	-3340,..	2.017035
17	suppressed	0.706649	-14516.754898	0.992478	3.300992e-01	3.006606e+00	-3786,..	1.775090
50	suppressed	0.706649	-12385.739588	1.000124	3.285459e-01	3.044093e+00	-5813,..	1.821217
155	suppressed	0.706577	-14871.704602	0.971259	3.423087e-01	2.837377e+00	-3427,..	1.836385
39	suppressed	0.706560	-14834.369569	0.984353	3.466659e-01	2.839485e+00	-3633,..	1.828666
57	suppressed	0.706538	-14560.121811	0.977939	3.508539e-01	2.787310e+00	-3520,..	1.913195
10	suppressed	0.706332	-14348.438630	0.976600	3.543698e-01	2.755879e+00	-3950,..	1.891869
190	suppressed	0.706238	-15146.146701	0.979361	3.513099e-01	2.787740e+00	-3404,..	1.779870
59	suppressed	0.706191	-14736.686310	0.973408	3.386674e-01	2.874229e+00	-3279,..	1.843195
189	suppressed	0.706132	-14266.456449	0.992509	3.298950e-01	3.008561e+00	-3416,..	1.956562
132	suppressed	0.706020	-15124.490816	0.991018	3.284176e-01	3.017553e+00	-3341,..	1.679107
170	suppressed	0.705971	-15037.345084	0.980479	3.519534e-01	2.785820e+00	-3263,..	1.822034
88	suppressed	0.705764	-15043.458779	0.982262	3.280813e-01	2.993960e+00	-3285,..	1.662796
117	suppressed	0.705724	-15392.428503	0.985658	3.399299e-01	2.899592e+00	-3077,..	1.660002
197	suppressed	0.705694	-15355.968739	0.995124	3.378709e-01	2.945280e+00	-3235,..	1.636952
178	suppressed	0.705510	-11919.848967	0.974559	3.512351e-01	2.774662e+00	-6017,..	2.081734
176	suppressed	0.704807	-12282.556756	0.987461	3.497943e-01	2.822975e+00	-6343,..	1.853654
40	suppressed	0.704783	-12441.101308	0.985508	3.538028e-01	2.785473e+00	-5786,..	1.915172
86	suppressed	0.704288	-16117.681316	0.988564	3.593306e-01	2.751127e+00	-2946,..	1.499881
66	suppressed	0.703571	-15843.789595	0.986702	3.731855e-01	2.643999e+00	-2915,..	1.486631
174	suppressed	0.703511	-16671.185196	0.990813	3.838566e-01	2.581205e+00	-2717,..	1.411135
25	suppressed	0.701207	-17758.332414	0.995580	4.279674e-01	2.326299e+00	-2219,..	1.100871
79	suppressed	0.701059	-5687.560070	0.977325	3.455221e-01	2.828547e+00	-9669,..	2.695829
179	suppressed	0.693248	-17615.624014	0.993124	4.555975e-01	2.179827e+00	-2091,..	1.166737
186	suppressed	0.691515	-16146.337987	1.006219	2.862331e-01	3.515384e+00	952,..	1.437103
168	suppressed	0.691327	-15643.989028	0.996489	2.532285e-01	3.935136e+00	1011,..	1.550288
164	suppressed	0.691300	-13069.601688	0.999684	2.780287e-01	3.595614e+00	1013,..	1.478663
156	suppressed	0.691226	-16913.401053	1.003214	3.380721e-01	2.967457e+00	997,..	1.218829
199	suppressed	0.690879	-15436.036410	0.993807	2.997857e-01	3.315060e+00	1128,..	1.381782
26	suppressed	0.690621	-13797.222377	0.995097	3.711335e-01	2.681237e+00	994,..	1.259845
181	suppressed	0.690596	-15920.557199	0.998980	4.038654e-01	2.473546e+00	1023,..	1.132092
81	suppressed	0.690278	-17542.369990	1.007353	3.258259e-01	3.091692e+00	2059,..	1.091616
162	suppressed	0.690084	-12729.382776	0.991340	1.950888e-01	5.081478e+00	910,..	1.845683
77	suppressed	0.689858	-15244.207265	0.997759	2.320090e-01	4.300517e+00	3003,..	1.252288
93	suppressed	0.689778	-15784.842901	1.002332	1.925392e-01	5.205860e+00	3793,..	1.347076
177	suppressed	0.689680	-14850.846865	0.993328	1.568490e-01	6.333020e+00	4782,..	1.434956
198	suppressed	0.689632	-12986.871927	1.008315	1.267022e-01	7.958144e+00	6869,..	1.571041
194	suppressed	0.689611	-15602.098567	1.003485	1.592229e-01	6.302395e+00	4810,..	1.428945
33	suppressed	0.689609	-12278.138207	0.993908	2.039798e-01	4.872583e+00	823,..	1.806295
105	suppressed	0.689552	-13609.467077	0.992334	1.916921e-01	5.176705e+00	3309,..	1.355005
58	suppressed	0.689512	-17358.138957	1.005609	4.189782e-01	2.400146e+00	870,..	1.045546
110	suppressed	0.689484	-13592.997812	1.004792	1.353846e-01	7.421760e+00	5089,..	1.514374
91	suppressed	0.689472	-15107.581727	1.004360	2.613080e-01	3.843587e+00	1780,..	1.380881
42	suppressed	0.689452	-14880.210463	1.006580	1.454196e-01	6.921896e+00	5140,..	1.462380
175	suppressed	0.689431	-15614.763683	1.002749	1.481553e-01	6.768228e+00	5129,..	1.454054
131	suppressed	0.689420	-14486.356792	1.004350	1.332029e-01	7.540000e+00	9128,..	1.517692
37	suppressed	0.689415	-10870.138328	0.983454	1.084727e-01	9.066369e+00	7527,..	1.617936
138	suppressed	0.689389	-15210.219158	1.001756	1.411859e-01	7.095299e+00	7125,..	1.479178
163	suppressed	0.689322	-17016.173404	1.001110	2.271160e-01	4.407925e+00	3805,..	1.228709
23	suppressed	0.689285	-15334.121138	0.998403	1.601254e-01	6.235128e+00	6096,..	1.426229
139	suppressed	0.689275	-14968.738712	0.985607	1.611523e-01	6.115998e+00	5297,..	1.462571
136	suppressed	0.689245	-15624.305603	0.995569	1.637573e-01	6.079542e+00	3770,..	1.443532
47	suppressed	0.689201	-13897.680234	1.000110	1.110334e-01	9.007290e+00	6088,..	1.572170
84	suppressed	0.689147	-13428.710600	0.999244	1.644637e-01	6.075770e+00	4204,..	1.417729
7	suppressed	0.689108	-16304.036773	1.016735	2.024288e-01	5.022680e+00	5265,..	1.311394
15	suppressed	0.688938	-16839.026618	0.990299	2.106023e-01	4.702221e+00	3149,..	1.294219
134	suppressed	0.688918	-16293.358489	1.007546	1.944064e-01	5.182676e+00	6301,..	1.329639
97	suppressed	0.688906	-17070.717136	0.994390	2.213411e-01	4.492569e+00	4240,..	1.222828
72	suppressed	0.688881	-16756.222537	0.995600	2.521916e-01	3.947792e+00	3284,..	1.182881
143	suppressed	0.688853	-17184.192814	1.003904	3.492510e-01	2.874449e+00	724,..	1.212848
9	suppressed	0.688788	-16929.817999	0.999057	2.282671e-01	4.376702e+00	4598,..	1.233442
103	suppressed	0.688759	-15021.762842	1.032351	1.863520e-01	5.539792e+00	7979,..	1.414699
95	suppressed	0.688724	-15716.280579	1.005432	1.903584e-01	5.281787e+00	7310,..	1.375747
123	suppressed	0.688712	-16358.697414	0.999613	4.169621e-01	2.397371e+00	1103,..	1.001356
133	suppressed	0.688627	-16714.913398	1.000219	2.615284e-01	3.824512e+00	3078,..	1.170829
83	suppressed	0.688541	-13193.813234	1.003698	9.415072e-02	1.066054e+01	7463,..	1.722177
140	suppressed	0.688419	-16201.718296	0.996574	2.026823e-01	4.916926e+00	7043,..	1.312820
193	suppressed	0.688277	-12711.452094	1.036695	9.553550e-02	1.085141e+01	8309,..	1.678374
4	suppressed	0.688199	-14171.902627	1.007360	1.889832e-01	5.330423e+00	4795,..	1.387307
145	suppressed	0.687912	-16847.358892	1.002725	1.902105e-01	5.271658e+00	4759,..	1.292409
191	suppressed	0.687045	-14053.780496	1.023599	1.200040e-01	8.529711e+00	3649,..	1.613660
182	suppressed	0.687007	-14575.935787	0.990127	1.109742e-01	8.922138e+00	3799,..	1.639604
118	suppressed	0.686961	-14280.962116	0.989060	1.593389e-01	6.207275e+00	5230,..	1.522630
0	suppressed	0.686316	-18402.724732	1.006706	4.111071e-01	2.448769e+00	468,..	0.951009
165	suppressed	0.686287	-18092.935773	1.011781	5.367038e-01	1.885176e+00	552,..	0.863286
38	suppressed	0.686258	-15721.053149	0.997985	4.249321e-01	2.348576e+00	59,..	1.047510
137	suppressed	0.685782	-16064.398904	0.994807	2.989403e-01	3.327778e+00	137,..	1.353409
74	suppressed	0.685781	-13890.959743	1.001878	2.735926e-01	3.661935e+00	6,..	1.409137
99	suppressed	0.685694	-18151.931068	0.991348	4.382392e-01	2.262116e+00	165,..	0.962420
19	suppressed	0.685242	-11562.276563	1.000928	8.590960e-02	1.165095e+01	3758,..	1.756323
173	suppressed	0.685103	-14084.217988	0.997685	2.207472e-01	4.519580e+00	-78,..	1.579495
111	suppressed	0.684769	-15319.082471	1.008228	1.722941e-01	5.851782e+00	5109,..	1.495716
3	suppressed	0.684431	-15401.274495	1.007629	1.911576e-01	5.271197e+00	677,..	1.498431
121	suppressed	0.684407	-13937.650487	0.992099	2.063439e-01	4.807990e+00	729,..	1.446902
73	suppressed	0.684283	-12813.992369	0.985977	2.417026e-01	4.079299e+00	1026,..	1.320159
128	suppressed	0.684202	-15141.216197	1.003128	2.139854e-01	4.687836e+00	-241,..	1.564046
106	suppressed	0.684201	-11086.710726	0.997053	1.895628e-01	5.259753e+00	-153,..	1.716338
154	suppressed	0.684183	-13200.388406	1.002949	1.029199e-01	9.744952e+00	697,..	1.837202
45	suppressed	0.684162	-15173.224665	0.997116	1.603302e-01	6.219140e+00	904,..	1.560863
129	suppressed	0.684059	-15488.045282	1.009765	2.650104e-01	3.810284e+00	611,..	1.314235
148	suppressed	0.683925	-17228.786501	0.999101	2.611584e-01	3.825651e+00	478,..	1.230638
48	suppressed	0.683843	-12645.591557	0.995942	1.936052e-01	5.144191e+00	-76,..	1.674068
18	suppressed	0.683652	-14474.068822	0.996068	1.545802e-01	6.443700e+00	3415,..	1.563079
6	suppressed	0.683599	-14688.853454	0.996305	1.613292e-01	6.175602e+00	4630,..	1.549996
160	suppressed	0.682889	-17833.357924	1.004205	3.432141e-01	2.925884e+00	683,..	1.044587
12	suppressed	0.682880	-14569.677210	1.002315	1.460788e-01	6.861466e+00	293,..	1.648877
46	suppressed	0.682860	-14360.525184	0.992889	3.322933e-01	2.987989e+00	1016,..	1.098568
127	suppressed	0.682661	-15937.728140	1.000485	1.556259e-01	6.428782e+00	3187,..	1.533165
51	suppressed	0.682285	-13358.780025	1.002176	8.859939e-02	1.131132e+01	3474,..	1.810449
53	suppressed	0.682245	-16159.402162	1.002702	2.726855e-01	3.677137e+00	-85,..	1.328096
157	suppressed	0.682244	-16727.249768	0.997462	2.422436e-01	4.117597e+00	967,..	1.239346
147	suppressed	0.682243	-10522.042460	0.986185	1.273125e-02	7.746174e+01	9101,..	2.018860
85	suppressed	0.681994	-13788.694471	0.987880	7.037748e-02	1.403688e+01	3900,..	1.875761
52	suppressed	0.681892	-13230.769258	1.005784	6.794992e-02	1.480185e+01	3584,..	1.869381
61	suppressed	0.681854	-12949.802093	1.078937	3.664788e-01	2.944063e+00	1165,..	1.333286
196	suppressed	0.681745	-11542.925092	0.991728	3.265836e-02	3.036676e+01	5931,..	2.082934
82	suppressed	0.681706	-16721.547422	1.008883	2.154218e-01	4.683292e+00	2154,..	1.336659
125	suppressed	0.681639	-1996.077350	1.003199	1.459453e-02	6.873802e+01	16535,..	2.232660
22	suppressed	0.681514	-17385.697656	0.999252	4.608787e-01	2.168145e+00	188,..	0.989101
35	suppressed	0.681338	-11532.083488	1.009510	5.626905e-02	1.794078e+01	2213,..	1.937487
56	suppressed	0.681278	-11249.272314	0.999153	7.780888e-02	1.284111e+01	1833,..	1.817969
159	suppressed	0.681234	-10414.610430	1.008112	6.946986e-02	1.451149e+01	1106,..	1.855901
55	suppressed	0.681200	-15262.818769	0.992735	1.845043e-01	5.380555e+00	2006,..	1.428374
135	suppressed	0.680914	-17342.398900	1.000921	3.200151e-01	3.127732e+00	2799,..	1.143231
2	suppressed	0.680594	-17428.994679	1.005258	5.347457e-01	1.879881e+00	115,..	0.826383
116	suppressed	0.680548	-12977.217263	1.009804	1.789104e-01	5.644190e+00	1113,..	1.403936
54	suppressed	0.680547	-14423.721571	1.000461	1.015597e-01	9.850965e+00	1511,..	1.701792
187	suppressed	0.680056	-17443.841353	0.995991	3.956357e-01	2.517445e+00	-1262,..	1.141352
112	suppressed	0.679939	-12477.176909	0.992692	2.108629e-01	4.707758e+00	993,..	1.459250
76	suppressed	0.679898	-10564.005567	0.978742	4.227548e-02	2.315153e+01	2989,..	2.119125
5	suppressed	0.678769	-14572.544316	0.998531	9.389081e-02	1.063503e+01	1286,..	1.752676
100	suppressed	0.678695	-15289.721683	1.004637	1.653401e-01	6.076186e+00	480,..	1.711596
167	suppressed	0.678046	-19724.382139	1.015811	5.726492e-01	1.773880e+00	1528,..	0.556397
124	suppressed	0.677731	-12629.052363	1.004765	8.832236e-02	1.137611e+01	1338,..	1.644186
67	suppressed	0.677316	-14177.430824	1.007285	1.029000e-01	9.788969e+00	1343,..	1.608008
180	suppressed	0.676378	-5191.839972	0.999909	1.740217e-01	5.745887e+00	-4690,..	2.882839
126	suppressed	0.675290	-9392.032255	1.001299	2.211596e-01	4.527493e+00	270,..	1.803445
36	suppressed	0.674456	-4005.721485	1.024127	2.199928e-02	4.655276e+01	-501,..	2.249018
90	suppressed	0.674130	-1477.444289	1.007652	1.485172e-01	6.784752e+00	161,..	2.049748
27	suppressed	0.672611	-10589.702972	0.995451	4.642809e-02	2.144070e+01	577,..	2.114644
20	suppressed	0.671560	-11984.804438	1.011126	3.853435e-02	2.623961e+01	225,..	2.133795
71	suppressed	0.671365	-10616.850003	1.002407	3.113391e-02	3.219662e+01	689,..	2.153088
153	suppressed	0.664325	-16626.876826	0.989522	1.990431e-01	4.971393e+00	-1521,..	1.287714
1	suppressed	0.657098	-12774.305450	1.012679	1.648924e-01	6.141451e+00	-5217,..	1.893854
102	suppressed	0.656719	-12330.372926	1.003932	1.359755e-01	7.383182e+00	-5682,..	2.019963
11	suppressed	0.633816	-16151.612530	1.283313	3.320448e-01	3.864880e+00	-903,..	1.450578
115	suppressed	0.622930	-15283.432578	0.806148	2.157546e-01	3.736412e+00	-2852,..	1.372022
185	suppressed	0.611097	-3939.913448	0.961641	3.961881e-02	2.427233e+01	7606,..	2.687387
122	suppressed	0.544747	-1761.572649	1.010639	1.255845e-03	8.047480e+02	3010,..	3.692221
64	suppressed	0.540184	20091.732530	1.003961	8.829830e-07	1.137010e+06	-5939,..	4.375666
169	suppressed	0.537554	7974.476636	1.002667	1.379499e-02	7.268342e+01	-1750,..	3.566194
92	suppressed	0.510130	10238.574405	0.996255	3.308198e-04	3.011475e+03	10172,..	3.958166

	model	r	G0	max binding	min binding	ratio	energies	information	PCA1	PCA2
184	suppressed	0.709102	-8369.682846	0.961111	0.305767	3.143276	-6305,..	2.372494	17988.736468	-39.460457
60	suppressed	0.709079	-6483.433347	0.969193	0.305267	3.174901	-6098,..	2.434594	20391.888512	913.429057
68	suppressed	0.708971	-9111.459686	0.970340	0.311781	3.112246	-5960,..	2.360772	16887.932155	-386.841108
63	suppressed	0.708837	-8917.596818	0.968301	0.311506	3.108449	-4911,..	2.374628	16443.067048	616.642582
144	suppressed	0.708727	-9858.288782	0.975751	0.310855	3.138928	-6247,..	2.281839	15918.681832	-617.144040
114	suppressed	0.708587	-10537.550819	0.977608	0.311074	3.142686	-5771,..	2.270755	14895.509678	-651.561250
108	suppressed	0.708546	-11716.668946	0.982724	0.324349	3.029831	-5044,..	2.231729	12947.405513	-655.573760
107	suppressed	0.708505	-10799.940918	0.983687	0.320787	3.066482	-4894,..	2.240343	14256.905212	40.058050
98	suppressed	0.708344	-9607.492522	0.961975	0.321038	2.996457	-6141,..	2.295675	16120.051120	-1053.813118
43	suppressed	0.708297	-12027.574721	0.980347	0.323265	3.032646	-4163,..	2.262535	12051.655413	-149.126292
69	suppressed	0.708239	-12542.382842	0.976880	0.327051	2.986939	-4521,..	2.152745	11728.799497	-696.268337
75	suppressed	0.708180	-10861.826932	0.971977	0.321625	3.022080	-6545,..	2.108372	14972.576941	-1500.001547
171	suppressed	0.708176	-12574.545868	0.961221	0.316859	3.033594	-4297,..	2.181432	11294.846696	-482.868040
130	suppressed	0.708125	-10091.505729	0.960229	0.328838	2.920067	-6843,..	2.222277	15818.802865	-1867.920673
96	suppressed	0.708117	-12468.372975	0.978621	0.334799	2.923006	-4713,..	2.165810	11785.107054	-946.389459
32	suppressed	0.708044	-10567.029564	0.990502	0.330981	2.992622	-6730,..	2.128858	15582.704727	-1408.958697
94	suppressed	0.708014	-6377.124055	0.975092	0.300255	3.247548	-7061,..	2.493042	17823.638354	896.963707
14	suppressed	0.707989	-12696.448826	0.978061	0.317190	3.083517	-3945,..	2.186752	11140.409781	-188.148214
44	suppressed	0.707945	-10909.425345	0.978234	0.327714	2.985023	-6109,..	2.180312	14555.684536	-1390.356182
49	suppressed	0.707899	-11768.069798	0.976074	0.312281	3.125624	-5124,..	2.075452	13278.292793	-277.214157
28	suppressed	0.707751	-10773.722431	0.989636	0.326360	3.032342	-6965,..	2.027353	15506.318872	-1542.864470
142	suppressed	0.707746	-11610.950337	0.966895	0.319337	3.027819	-5455,..	2.148604	13429.938844	-1284.337936
21	suppressed	0.707740	-12036.418943	0.984355	0.334559	2.942251	-5711,..	2.061603	13102.139528	-1443.360373
8	suppressed	0.707738	-13148.663195	0.978205	0.314652	3.108849	-3911,..	2.085305	10897.973550	-263.869682
195	suppressed	0.707713	-9692.825219	0.978272	0.319962	3.057459	-7421,..	2.110664	17109.744430	-1946.071426
149	suppressed	0.707620	-13161.544905	0.973257	0.338790	2.872742	-4232,..	2.094864	10617.249614	-914.241602
87	suppressed	0.707618	-13553.509385	0.977256	0.332720	2.937175	-3862,..	2.075937	10011.980106	-723.830110
104	suppressed	0.707603	-12612.377303	0.975036	0.318167	3.064541	-4193,..	2.104714	11188.590870	135.225999
152	suppressed	0.707585	-11501.727201	0.973607	0.344324	2.827590	-5912,..	2.155174	13559.385909	-1690.385163
141	suppressed	0.707575	-9973.717176	0.985053	0.340488	2.893062	-7111,..	2.125851	16569.518635	-1929.464724
16	suppressed	0.707554	-13159.914853	0.978340	0.337995	2.894541	-4103,..	2.122826	10419.538375	-734.708127
146	suppressed	0.707519	-11236.377197	0.979721	0.329021	2.977684	-6075,..	2.101092	14279.141869	-1336.063082
65	suppressed	0.707508	-13867.033125	0.978684	0.336970	2.904360	-4009,..	1.985818	9838.838040	-726.692712
151	suppressed	0.707465	-12529.011975	0.983077	0.327270	3.003867	-3756,..	2.150554	11369.953430	-285.043877
13	suppressed	0.707361	-12294.144056	0.969659	0.332895	2.912805	-5368,..	2.036012	12540.229977	-1344.007264
120	suppressed	0.707344	-14248.122927	0.974966	0.339749	2.869665	-3806,..	1.933827	9220.165879	-611.947955
183	suppressed	0.707320	-13547.375868	0.991946	0.319046	3.109102	-4466,..	1.959040	10609.975431	-742.146955
192	suppressed	0.707311	-14071.357127	0.987226	0.342600	2.881567	-4029,..	1.942493	9673.672157	-787.815013
172	suppressed	0.707291	-14202.033901	0.985084	0.327455	3.008301	-3611,..	1.929793	9373.320638	-211.111435
109	suppressed	0.707267	-13278.828687	0.990597	0.326223	3.036563	-4060,..	2.121135	10309.577017	-651.578072
29	suppressed	0.707244	-11582.318621	0.974590	0.322279	3.024056	-5820,..	2.091526	13566.264422	-1485.239749
70	suppressed	0.707232	-14426.213016	0.977949	0.331438	2.950627	-3647,..	1.908878	8976.957737	-370.748044
24	suppressed	0.707171	-13452.424348	0.969906	0.325620	2.978647	-4469,..	1.867069	11042.295215	-487.286521
31	suppressed	0.707094	-14150.017156	0.972750	0.344158	2.826460	-3919,..	1.960341	9255.479365	-842.105187
188	suppressed	0.707090	-13118.532865	0.984173	0.320385	3.071847	-3875,..	2.115472	10786.489404	-463.035278
62	suppressed	0.707049	-14663.897962	0.976094	0.335843	2.906403	-3559,..	1.857300	8703.187417	-384.511327
34	suppressed	0.707022	-14141.075086	0.973570	0.341512	2.850763	-4104,..	1.887372	9576.593530	-841.410279
161	suppressed	0.706965	-14284.886682	0.974979	0.332367	2.933444	-3876,..	1.898711	9191.852405	-693.988669
78	suppressed	0.706961	-13842.987845	0.981583	0.330590	2.969190	-4185,..	1.875746	10164.313831	-395.998395
41	suppressed	0.706938	-14000.601372	0.985296	0.347603	2.834546	-4135,..	1.951028	9696.865847	-1041.866914
89	suppressed	0.706883	-14046.231784	0.970092	0.348089	2.786907	-3878,..	1.977826	9292.257971	-985.584328
101	suppressed	0.706842	-14628.003671	0.974030	0.335238	2.905489	-3577,..	1.819633	8855.402579	-269.816197
30	suppressed	0.706813	-14424.234893	0.970130	0.344662	2.814729	-3763,..	1.887071	9008.289970	-664.068733
119	suppressed	0.706809	-14484.700741	0.978064	0.324069	3.018073	-3443,..	1.896427	8883.805088	-386.689879
113	suppressed	0.706800	-10651.474175	1.001419	0.318557	3.143612	-6723,..	1.865427	16122.804963	-891.883451
80	suppressed	0.706772	-14603.283640	0.977040	0.335609	2.911243	-3647,..	1.819496	8865.562810	-331.501238
166	suppressed	0.706756	-13080.277537	0.967184	0.326186	2.965130	-3613,..	1.940005	10915.406620	522.548226
158	suppressed	0.706714	-13754.512853	0.973007	0.312595	3.112678	-4105,..	1.823113	10366.679558	-83.706624
150	suppressed	0.706676	-13905.816204	0.980039	0.323361	3.030794	-3340,..	2.017035	9531.045819	-319.682474
17	suppressed	0.706649	-14516.754898	0.992478	0.330099	3.006606	-3786,..	1.775090	9313.163959	-156.206709
50	suppressed	0.706649	-12385.739588	1.000124	0.328546	3.044093	-5813,..	1.821217	13251.890204	-948.244839
155	suppressed	0.706577	-14871.704602	0.971259	0.342309	2.837377	-3427,..	1.836385	8236.834081	-613.066781
39	suppressed	0.706560	-14834.369569	0.984353	0.346666	2.839485	-3633,..	1.828666	8510.064049	-559.178396
57	suppressed	0.706538	-14560.121811	0.977939	0.350854	2.787310	-3520,..	1.913195	8596.633855	-796.063170
10	suppressed	0.706332	-14348.438630	0.976600	0.354370	2.755879	-3950,..	1.891869	9118.917543	-1099.532794
190	suppressed	0.706238	-15146.146701	0.979361	0.351310	2.787740	-3404,..	1.779870	7940.328469	-578.273083
59	suppressed	0.706191	-14736.686310	0.973408	0.338667	2.874229	-3279,..	1.843195	8652.452251	-247.935300
189	suppressed	0.706132	-14266.456449	0.992509	0.329895	3.008561	-3416,..	1.956562	9061.110316	-611.349095
132	suppressed	0.706020	-15124.490816	0.991018	0.328418	3.017553	-3341,..	1.679107	8422.090086	41.075251
170	suppressed	0.705971	-15037.345084	0.980479	0.351953	2.785820	-3263,..	1.822034	7941.943415	-699.557822
88	suppressed	0.705764	-15043.458779	0.982262	0.328081	2.993960	-3285,..	1.662796	8406.987892	263.222915
117	suppressed	0.705724	-15392.428503	0.985658	0.339930	2.899592	-3077,..	1.660002	7906.449252	38.793113
197	suppressed	0.705694	-15355.968739	0.995124	0.337871	2.945280	-3235,..	1.636952	8052.993739	131.759162
178	suppressed	0.705510	-11919.848967	0.974559	0.351235	2.774662	-6017,..	2.081734	12799.283068	-2194.302833
176	suppressed	0.704807	-12282.556756	0.987461	0.349794	2.822975	-6343,..	1.853654	13141.759251	-1982.224209
40	suppressed	0.704783	-12441.101308	0.985508	0.353803	2.785473	-5786,..	1.915172	12555.907053	-2005.633858
86	suppressed	0.704288	-16117.681316	0.988564	0.359331	2.751127	-2946,..	1.499881	6960.088832	105.578610
66	suppressed	0.703571	-15843.789595	0.986702	0.373186	2.643999	-2915,..	1.486631	7441.454196	381.029731
174	suppressed	0.703511	-16671.185196	0.990813	0.383857	2.581205	-2717,..	1.411135	6036.140476	-113.561488
25	suppressed	0.701207	-17758.332414	0.995580	0.427967	2.326299	-2219,..	1.100871	4871.096497	223.296194
79	suppressed	0.701059	-5687.560070	0.977325	0.345522	2.828547	-9669,..	2.695829	20156.895412	-3690.730365
179	suppressed	0.693248	-17615.624014	0.993124	0.455598	2.179827	-2091,..	1.166737	4350.829720	-501.274535
186	suppressed	0.691515	-16146.337987	1.006219	0.286233	3.515384	952,..	1.437103	-8030.463404	-4607.283486
168	suppressed	0.691327	-15643.989028	0.996489	0.253229	3.935136	1011,..	1.550288	-8047.545094	-5145.121917
164	suppressed	0.691300	-13069.601688	0.999684	0.278029	3.595614	1013,..	1.478663	-13762.710963	-7542.301777
156	suppressed	0.691226	-16913.401053	1.003214	0.338072	2.967457	997,..	1.218829	-7210.382605	-4059.863095
199	suppressed	0.690879	-15436.036410	0.993807	0.299786	3.315060	1128,..	1.381782	-10376.910416	-4737.296761
26	suppressed	0.690621	-13797.222377	0.995097	0.371134	2.681237	994,..	1.259845	948.184353	15729.145744
181	suppressed	0.690596	-15920.557199	0.998980	0.403865	2.473546	1023,..	1.132092	-512.402640	12898.333589
81	suppressed	0.690278	-17542.369990	1.007353	0.325826	3.091692	2059,..	1.091616	-8972.122159	-915.329515
162	suppressed	0.690084	-12729.382776	0.991340	0.195089	5.081478	910,..	1.845683	-10531.822198	-8408.354218
77	suppressed	0.689858	-15244.207265	0.997759	0.232009	4.300517	3003,..	1.252288	-13232.632586	-2368.160018
93	suppressed	0.689778	-15784.842901	1.002332	0.192539	5.205860	3793,..	1.347076	-12185.021913	-1035.336004
177	suppressed	0.689680	-14850.846865	0.993328	0.156849	6.333020	4782,..	1.434956	-13733.972834	-1295.737101
198	suppressed	0.689632	-12986.871927	1.008315	0.126702	7.958144	6869,..	1.571041	-16758.924100	-2027.061466
194	suppressed	0.689611	-15602.098567	1.003485	0.159223	6.302395	4810,..	1.428945	-12295.416685	-628.754509
33	suppressed	0.689609	-12278.138207	0.993908	0.203980	4.872583	823,..	1.806295	-11488.238423	-8730.740490
105	suppressed	0.689552	-13609.467077	0.992334	0.191692	5.176705	3309,..	1.355005	-15902.533693	-3492.175824
58	suppressed	0.689512	-17358.138957	1.005609	0.418978	2.400146	870,..	1.045546	-275.047407	9912.585816
110	suppressed	0.689484	-13592.997812	1.004792	0.135385	7.421760	5089,..	1.514374	-15763.480894	-2383.269832
91	suppressed	0.689472	-15107.581727	1.004360	0.261308	3.843587	1780,..	1.380881	-12033.016104	-3852.444167
42	suppressed	0.689452	-14880.210463	1.006580	0.145420	6.921896	5140,..	1.462380	-13567.265276	-1411.840267
175	suppressed	0.689431	-15614.763683	1.002749	0.148155	6.768228	5129,..	1.454054	-12278.057339	-350.524927
131	suppressed	0.689420	-14486.356792	1.004350	0.133203	7.540000	9128,..	1.517692	-13829.211131	101.223296
37	suppressed	0.689415	-10870.138328	0.983454	0.108473	9.066369	7527,..	1.617936	-20157.999072	-3678.411790
138	suppressed	0.689389	-15210.219158	1.001756	0.141186	7.095299	7125,..	1.479178	-12716.695859	-8.047174
163	suppressed	0.689322	-17016.173404	1.001110	0.227116	4.407925	3805,..	1.228709	-10099.970174	299.945004
23	suppressed	0.689285	-15334.121138	0.998403	0.160125	6.235128	6096,..	1.426229	-12760.198282	-390.276762
139	suppressed	0.689275	-14968.738712	0.985607	0.161152	6.115998	5297,..	1.462571	-13528.407177	-648.852833
136	suppressed	0.689245	-15624.305603	0.995569	0.163757	6.079542	3770,..	1.443532	-11868.389636	-1316.610979
47	suppressed	0.689201	-13897.680234	1.000110	0.111033	9.007290	6088,..	1.572170	-15093.067312	-1599.385039
84	suppressed	0.689147	-13428.710600	0.999244	0.164464	6.075770	4204,..	1.417729	-16165.493664	-3291.329141
7	suppressed	0.689108	-16304.036773	1.016735	0.202429	5.022680	5265,..	1.311394	-11564.508088	144.642129
15	suppressed	0.688938	-16839.026618	0.990299	0.210602	4.702221	3149,..	1.294219	-9719.281488	-601.259062
134	suppressed	0.688918	-16293.358489	1.007546	0.194406	5.182676	6301,..	1.329639	-11240.047609	653.065478
97	suppressed	0.688906	-17070.717136	0.994390	0.221341	4.492569	4240,..	1.222828	-9842.720773	434.210854
72	suppressed	0.688881	-16756.222537	0.995600	0.252192	3.947792	3284,..	1.182881	-11022.350227	-168.612044
143	suppressed	0.688853	-17184.192814	1.003904	0.349251	2.874449	724,..	1.212848	-6783.152599	-3831.293475
9	suppressed	0.688788	-16929.817999	0.999057	0.228267	4.376702	4598,..	1.233442	-10119.319360	451.592529
103	suppressed	0.688759	-15021.762842	1.032351	0.186352	5.539792	7979,..	1.414699	-13797.869750	123.904654
95	suppressed	0.688724	-15716.280579	1.005432	0.190358	5.281787	7310,..	1.375747	-12194.990607	541.750598
123	suppressed	0.688712	-16358.697414	0.999613	0.416962	2.397371	1103,..	1.001356	-10978.773308	-3042.620950
133	suppressed	0.688627	-16714.913398	1.000219	0.261528	3.824512	3078,..	1.170829	-11061.917755	-328.276312
83	suppressed	0.688541	-13193.813234	1.003698	0.094151	10.660543	7463,..	1.722177	-14925.290440	-625.188691
140	suppressed	0.688419	-16201.718296	0.996574	0.202682	4.916926	7043,..	1.312820	-11522.024944	928.752711
193	suppressed	0.688277	-12711.452094	1.036695	0.095536	10.851408	8309,..	1.678374	-17111.195884	-1529.510671
4	suppressed	0.688199	-14171.902627	1.007360	0.188983	5.330423	4795,..	1.387307	-15658.897913	-1668.410344
145	suppressed	0.687912	-16847.358892	1.002725	0.190211	5.271658	4759,..	1.292409	-10438.952588	522.198433
191	suppressed	0.687045	-14053.780496	1.023599	0.120004	8.529711	3649,..	1.613660	-13963.670293	-3391.008709
182	suppressed	0.687007	-14575.935787	0.990127	0.110974	8.922138	3799,..	1.639604	-12521.087606	-2486.377752
118	suppressed	0.686961	-14280.962116	0.989060	0.159339	6.207275	5230,..	1.522630	-14882.242897	-875.005921
0	suppressed	0.686316	-18402.724732	1.006706	0.411107	2.448769	468,..	0.951009	-72.108522	7512.865021
165	suppressed	0.686287	-18092.935773	1.011781	0.536704	1.885176	552,..	0.863286	-553.448714	9048.117914
38	suppressed	0.686258	-15721.053149	0.997985	0.424932	2.348576	59,..	1.047510	1151.333170	-8026.376234
137	suppressed	0.685782	-16064.398904	0.994807	0.298940	3.327778	137,..	1.353409	714.362544	-7789.337444
74	suppressed	0.685781	-13890.959743	1.001878	0.273593	3.661935	6,..	1.409137	1486.368619	-10880.920070
99	suppressed	0.685694	-18151.931068	0.991348	0.438239	2.262116	165,..	0.962420	143.099740	-4775.648947
19	suppressed	0.685242	-11562.276563	1.000928	0.085910	11.650946	3758,..	1.756323	-17150.729304	-5979.677845
173	suppressed	0.685103	-14084.217988	0.997685	0.220747	4.519580	-78,..	1.579495	776.186502	-10829.826932
111	suppressed	0.684769	-15319.082471	1.008228	0.172294	5.851782	5109,..	1.495716	-13541.263052	319.247043
3	suppressed	0.684431	-15401.274495	1.007629	0.191158	5.271197	677,..	1.498431	-3887.050902	11832.690672
121	suppressed	0.684407	-13937.650487	0.992099	0.206344	4.807990	729,..	1.446902	-4714.716413	15926.541119
73	suppressed	0.684283	-12813.992369	0.985977	0.241703	4.079299	1026,..	1.320159	-2980.046488	19435.732685
128	suppressed	0.684202	-15141.216197	1.003128	0.213985	4.687836	-241,..	1.564046	780.584262	-9183.223335
106	suppressed	0.684201	-11086.710726	0.997053	0.189563	5.259753	-153,..	1.716338	2233.209004	-14585.430936
154	suppressed	0.684183	-13200.388406	1.002949	0.102920	9.744952	697,..	1.837202	3049.970181	13541.572505
45	suppressed	0.684162	-15173.224665	0.997116	0.160330	6.219140	904,..	1.560863	-3971.550873	12201.658411
129	suppressed	0.684059	-15488.045282	1.009765	0.265010	3.810284	611,..	1.314235	-4093.749215	13096.400435
148	suppressed	0.683925	-17228.786501	0.999101	0.261158	3.825651	478,..	1.230638	-3374.787325	8589.258295
48	suppressed	0.683843	-12645.591557	0.995942	0.193605	5.144191	-76,..	1.674068	1881.713401	-12311.944500
18	suppressed	0.683652	-14474.068822	0.996068	0.154580	6.443700	3415,..	1.563079	-14856.213196	-711.253382
6	suppressed	0.683599	-14688.853454	0.996305	0.161329	6.175602	4630,..	1.549996	-14574.284375	-186.800155
160	suppressed	0.682889	-17833.357924	1.004205	0.343214	2.925884	683,..	1.044587	-3318.901153	8368.631468
12	suppressed	0.682880	-14569.677210	1.002315	0.146079	6.861466	293,..	1.648877	-3529.818448	10832.740684
46	suppressed	0.682860	-14360.525184	0.992889	0.332293	2.987989	1016,..	1.098568	-4456.108653	16979.962915
127	suppressed	0.682661	-15937.728140	1.000485	0.155626	6.428782	3187,..	1.533165	-12237.046199	802.416662
51	suppressed	0.682285	-13358.780025	1.002176	0.088599	11.311318	3474,..	1.810449	-16126.711937	-524.810599
53	suppressed	0.682245	-16159.402162	1.002702	0.272686	3.677137	-85,..	1.328096	851.734209	-7557.616526
157	suppressed	0.682244	-16727.249768	0.997462	0.242244	4.117597	967,..	1.239346	-3805.017817	10295.656583
147	suppressed	0.682243	-10522.042460	0.986185	0.012731	77.461739	9101,..	2.018860	-16215.235576	-4802.045548
85	suppressed	0.681994	-13788.694471	0.987880	0.070377	14.036882	3900,..	1.875761	-14780.287494	57.114541
52	suppressed	0.681892	-13230.769258	1.005784	0.067950	14.801845	3584,..	1.869381	-16036.259210	-219.535820
61	suppressed	0.681854	-12949.802093	1.078937	0.366479	2.944063	1165,..	1.333286	1101.037027	-11917.682664
196	suppressed	0.681745	-11542.925092	0.991728	0.032658	30.366759	5931,..	2.082934	-17887.792289	75.640505
82	suppressed	0.681706	-16721.547422	1.008883	0.215422	4.683292	2154,..	1.336659	-11274.731301	646.542526
125	suppressed	0.681639	-1996.077350	1.003199	0.014595	68.738019	16535,..	2.232660	-32050.233630	-2989.085850
22	suppressed	0.681514	-17385.697656	0.999252	0.460879	2.168145	188,..	0.989101	676.382955	-5099.198526
35	suppressed	0.681338	-11532.083488	1.009510	0.056269	17.940776	2213,..	1.937487	-18378.049629	-350.928851
56	suppressed	0.681278	-11249.272314	0.999153	0.077809	12.841112	1833,..	1.817969	-19489.256940	-1677.140678
159	suppressed	0.681234	-10414.610430	1.008112	0.069470	14.511495	1106,..	1.855901	-20390.751257	-1226.931698
55	suppressed	0.681200	-15262.818769	0.992735	0.184504	5.380555	2006,..	1.428374	-13638.777553	-173.180604
135	suppressed	0.680914	-17342.398900	1.000921	0.320015	3.127732	2799,..	1.143231	-10647.593668	962.817510
2	suppressed	0.680594	-17428.994679	1.005258	0.534746	1.879881	115,..	0.826383	287.799554	-5408.307500
116	suppressed	0.680548	-12977.217263	1.009804	0.178910	5.644190	1113,..	1.403936	-4870.379817	18616.430871
54	suppressed	0.680547	-14423.721571	1.000461	0.101560	9.850965	1511,..	1.701792	-14086.914141	144.300659
187	suppressed	0.680056	-17443.841353	0.995991	0.395636	2.517445	-1262,..	1.141352	1475.293307	-4671.355430
112	suppressed	0.679939	-12477.176909	0.992692	0.210863	4.707758	993,..	1.459250	-3096.803002	20036.128168
76	suppressed	0.679898	-10564.005567	0.978742	0.042275	23.151530	2989,..	2.119125	-19279.233928	-2060.458376
5	suppressed	0.678769	-14572.544316	0.998531	0.093891	10.635028	1286,..	1.752676	-13347.699425	-397.128794
100	suppressed	0.678695	-15289.721683	1.004637	0.165340	6.076186	480,..	1.711596	455.529710	11016.567392
167	suppressed	0.678046	-19724.382139	1.015811	0.572649	1.773880	1528,..	0.556397	-6569.206957	1656.726880
124	suppressed	0.677731	-12629.052363	1.004765	0.088322	11.376107	1338,..	1.644186	-3908.012959	17552.049509
67	suppressed	0.677316	-14177.430824	1.007285	0.102900	9.788969	1343,..	1.608008	-3194.641493	14607.671961
180	suppressed	0.676378	-5191.839972	0.999909	0.174022	5.745887	-4690,..	2.882839	15518.112104	-7909.211882
126	suppressed	0.675290	-9392.032255	1.001299	0.221160	4.527493	270,..	1.803445	1494.543317	-14173.445001
36	suppressed	0.674456	-4005.721485	1.024127	0.021999	46.552764	-501,..	2.249018	-23030.789649	-10900.791625
90	suppressed	0.674130	-1477.444289	1.007652	0.148517	6.784752	161,..	2.049748	3158.878570	-21142.347173
27	suppressed	0.672611	-10589.702972	0.995451	0.046428	21.440698	577,..	2.114644	406.930309	17613.347175
20	suppressed	0.671560	-11984.804438	1.011126	0.038534	26.239610	225,..	2.133795	1904.593995	14842.245139
71	suppressed	0.671365	-10616.850003	1.002407	0.031134	32.196618	689,..	2.153088	1409.231304	16489.111142

	stage	protein	# probes	motif length	r	AUROC	G0	G0 fitted	ratio	max binding	min binding	energies	model
0	best repetition	T7 primase	3149	4	0.709102	0.829612	-8369.682846	True	3.143276	0.961111	0.305767	-6305,..	suppressed
1	mae optimzed	T7 primase	3149	4	0.695813	0.820490	-8369.682846	True	3.143276	0.957377	0.312217	-5884,..	suppressed
2	expanded, border	T7 primase	3149	5	0.717139	0.831264	-8730.333206	True	3.881376	0.987928	0.254530	467,..	suppressed

	stage	protein	# probes	motif length	r	AUROC	G0	G0 fitted	ratio	max binding	min binding	energies	model
0	best repetition	T7 primase	3149	4	0.709102	0.829612	-8369.682846	True	3.143276	0.961111	0.305767	-6305,..	suppressed
1	mae optimzed	T7 primase	3149	4	0.695813	0.820490	-8369.682846	True	3.143276	0.957377	0.312217	-5884,..	suppressed
2	expanded, border	T7 primase	3149	5	0.717139	0.831264	-8730.333206	True	3.881376	0.987928	0.254530	467,..	suppressed
3	final mae optimzed	T7 primase	3149	5	0.698074	0.816455	-8730.333206	True	3.881376	1.063557	0.313207	354,..	suppressed